Metadata-Version: 2.1
Name: mdmpy
Version: 0.0.13
Summary: A small example package
Home-page: https://github.com/justanothergithubber/mdmpy
Author: MDM Py Authors
Author-email: 3600019+justanothergithubber@users.noreply.github.com
License: UNKNOWN
Description: # MDM Py
        
        This package is a `Python` implementation of Marginal Distribution Models (MDMs), which can be used in Discrete Choice Modelling.
        
        ## Install
        
        This package should eventually be uploaded onto PyPI when the package is more ready. In that case:
        
        ```
        pip install mdmpy
        ```
        
        should work.
        
        ## How to use
        ### Simplest Case
        #### Gradient Descent
        In the simplest case, we will use the Multinomial Logit (MNL) model, which is used as a default. Assuming `numpy`, `scipy` and `pandas` are installed, we generate choice data assuming a random utility model:
        
        ```python
        from string import ascii_uppercase as letters
        import pandas as pd
        import scipy.stats as stats
        import numpy as np
        
        NUM_INDIV   = 57
        NUM_CHOICES = 3
        NUM_ATTR    = 4
        
        np.random.seed(2019)
        X = np.random.random((NUM_ATTR,NUM_INDIV*NUM_CHOICES))
        true_beta = np.random.random(NUM_ATTR)
        V = np.dot(true_beta.T,X)
        V = np.reshape(V,(NUM_INDIV,NUM_CHOICES))
        eps = stats.gumbel_r.rvs(size=NUM_INDIV*NUM_CHOICES)
        eps = np.reshape(eps,(NUM_INDIV,NUM_CHOICES))
        U = V+eps
        highest_util = np.argmax(U,1)
        
        df = pd.DataFrame(X.T)
        df['choice'] = [1 if idx==x else 0 for idx in highest_util for x in range(NUM_CHOICES)]
        df['individual'] = [indiv for indiv in range(NUM_INDIV) for _ in range(NUM_CHOICES)]
        df['altvar'] = [altlvl for _ in range(NUM_INDIV) for altlvl in letters[:NUM_CHOICES]]
        ```
        
        With this package, we will assume that `df` is the dataframe which is simply given to us. Instead of having the code itself find out how many individuals, choices and coefficients or attributes there are, we will simply feed them into the class. To perform a gradient descent with this class, we will use the `grad_desc` method.
        
        ```python
        import mdmpy
        
        # In a typical case one would load df before this line
        mdm = mdmpy.MDM(df,4,3,[0,1,2,3])
        np.random.seed(4)
        init_beta = np.random.random(4)
        grad_beta = mdm.grad_desc(init_beta)
        print(grad_beta)
        # expected output [0.30238122 0.07955214 0.86779824 0.50951981]
        ```
        
        #### Solver
        The `MDM` class acts as a wrapper and adds the necessary `pyomo` variables and sets to model the problem, but requires a solver. [IPOPT](https://projects.coin-or.org/Ipopt), an interior point solver, is recommended. If you have such a solver, it can be called. Assuming IPOPT is being used:
        
        ```python
        import mdmpy
        
        ipopt_exec_path = /path/to/ipopt
        mdm = mdmpy.MDM(df,4,3,[0,1,2,3])
        mdm.model_init()
        mdm.model_solve("ipopt",ipopt_exec_path)
        print([mdm.m.beta[idx].value for idx in mdm.m.beta])
        # expected output [0.30238834989235025, 0.07953888508425154, 0.8678050334295714, 0.5095096796373667]
        ```
        
        ## Todo
        
        1.  Add documentation.
            * Add more type hints, especially those involving Python builtins
        2.  Add tests.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
